A method, computer program, computer program product and system for representing visual information

ABSTRACT

The present disclosure relates to a computer implemented method for representing a data set comprising at least one n dimensional data element representing visual information, said method comprising obtaining ( 210 ) said data set, obtaining ( 220 ) a dictionary ensemble comprising a plurality of dictionaries each comprising at least one basis function ( 102 ), assigning ( 230 ) each at least one data element to a dictionary, wherein a set of basis functions represents an m dimensional transformation domain, transforming ( 240 ) the at least one data element with the corresponding dictionary of basis functions to the transformation domain wherein each data element is defined by an associated coefficient set, sparsifying ( 250 ) the coefficient sets, forming ( 260 ) the representation of the visual information comprising a coefficient data set and the corresponding dictionaries.

TECHNICAL FIELD

The present disclosure relates to representing visual information, suchas light field data.

BACKGROUND

Over the last decade, a field of computational photography, especiallylight field and multi-view imaging, has emerged and matured as a newparadigm in imaging and video technology. These technologies enable arange of novel applications ranging from advanced multi-dimensionalimage processing to cinematic editing, glasses free 3D display systems,single sensor light field cameras, spectral imaging and appearancecapturing. A challenge of using these new formats of visual informationis related to the size of the produced data. Two significant issuesarising due to large data sizes are the efficient storage of data andthe infrastructure required to transfer the captured data from thesensor systems. Data compression and image compression are twowell-established field of research dedicated to addressing challengeswith handling and storing large data sizes.

SUMMARY

A highly important and still unsolved challenge inherent to capture,storage and processing of high-dimensional data is to handle the verylarge data sizes.

The invention relates to a solution to this problem.

The invention relates to computer implemented method for representing adata set comprising n dimensional data elements representing visualinformation, comprising a step of obtaining visual information, a stepof obtaining an ensemble of dictionaries comprising sets of basisfunctions, a step of assigning data elements to dictionaries, a step oftransforming the data elements with the corresponding sets of basisfunctions into a transformation domain represented by sets ofcoefficients, a step of sparsifying said sets of coefficients and a stepof forming the representation of the visual information based on thesparsified coefficient sets and the corresponding dictionaries of basisfunctions. The step of forming the representation of the visualinformation may comprise compressing the sparsified coefficient sets.

One advantage with the method as defined above is that the compressionrate of visual information may be improved.

One advantage with the method as defined above is that thereconstruction error of compressed visual information may be decreased.

One advantage with the method as defined above is improved compressedsensing of visual information.

The invention as defined herein enables improved selectivereconstruction of compressed visual information.

The step of obtaining a data set comprising data elements of visualinformation may comprise obtaining multi-dimensional visual information.A data element comprises at least one data point. The visual informationmay comprise data elements and/or data points based on sensor data andpossibly interpolation of sensor data. The visual information maycomprise data elements and/or data points based on computer generatedimagery and possibly interpolation of computer generated imagery.

The method may be of particular use in the capture, storage andprocessing of very large data sets, such as high-dimensional data likeview angle data. The method may be used to compress any kind ofmultidimensional data. The method may use non-local clustering. Themethod may be used for non-local clustering for multi-dimensional datasets. The method allows for efficient local reconstruction of compressedpossibly multidimensional data.

The step of obtaining an ensemble of dictionaries comprising basisfunctions may comprise learning an ensemble of dictionaries. Theensemble of dictionaries comprises sets of basis functions representingthe n dimensional data elements of visual information in an mdimensional transformation domain. The dimensionality of thetransformation domain, m, may be equal to or larger than 2. Thedimensionality of the transformation domain, m, may be equal to orlarger than the dimensionality of the visual information, n. The presentdisclosure relates to a method for transformation to and from arbitrarydimensionalities. A set of basis functions may independently representthe visual information in its various dimensions in the transformationdomain. Transformation of data elements of visual information to amultidimensional transformation domain may allow specific data elementsto be reconstructed, such as reconstructing an individual frame of avideo as opposed to reconstructing the whole video. The visualinformation comprising n dimensional data elements representing visualinformation may each be assigned a dictionary. The data elements may beassigned based on sparsity. Assigning data elements based on sparsityallows all data elements within a certain range of sparsity to betransformed with a dictionary comprising a set of basis functionssuitable for transforming data elements within that range of sparsity toa transformation domain, wherein the data element is represented by anassociated coefficient set.

The term transformation domain as used herein relates to therepresentation of visual information by sets of coefficients andcorresponding sets of basis functions. A well-known transformation isachieved by the Fourier transform, with the basis function e^(−2πiωx),which converts a time function into a sum of sine waves of differentfrequencies, each of which represents a frequency component. Thefrequency components in the frequency-domain represent the timefunction. Similarly, in the present disclosure the sets of coefficientsrepresent the visual information in the transformation domain defined bythe basis functions of each dictionary.

The term visual information refers to both sensor data and computergenerated imagery.

The term “data element” as used herein relates to a patch of the visualinformation comprising at least one data point. A patch of an image isalso called an “image block” or a “window”. A patch may be a small pieceof an image, such as a 10×10 pixel area.

The term “data point” as used herein relates to the smallest unit ofdata, such as the red, green or blue value of a pixel.

The term coefficient set comprises a set of values that if inserted intothe set of basis functions describes a patch of the visual information.

At least part of the obtained ensemble of dictionaries may be created aspart of the step of obtaining an ensemble of dictionaries and/or createdbeforehand by a training process utilizing pre-clustered training visualinformation of the same dimensionality, n, as the visual information.The training process aims to create dictionaries forming as sparsecoefficient sets as possible for the pre-clusters of training visualinformation. The training visual information may be of the same type asthe visual information, such as both being either live action video oranimated video. The training process of creating the ensemble ofdictionaries may be a machine-learning assisted process. An existingensemble may be expended and/or reduced. The process of creating theensemble may be a process of expanding an existing ensemble ofdictionaries with additional dictionaries. The process of creating theensemble may be a process of limiting an existing ensemble ofdictionaries by removing existing dictionaries. The dictionary ensembletrained on a certain type of visual information is expected to show ahigh degree of sparsity in the coefficient sets representing similarvisual information in the transformation domain. The creation of sparsecoefficient sets and sparsification of said sets are important factorsfor efficient compression and sampling. The dictionary ensemble willnormally comprise a multitude of basis functions but have a very lowmemory footprint compared to the data set size, hence encoding anddecoding efficiency may be substantially improved.

When it comes to the step of assigning each data element to adictionary, the data elements may be assigned to dictionaries based onsparsity. The method comprises a step of transforming each data elementto the transformation domain, wherein each data element is representedby one dictionary of basis functions and a set of coefficients. Thecoefficient sets, representing the data elements in the transformationdomain defined by the set of basis functions of the correspondingdictionary, comprises coefficient values. Coefficient values close tozero, under a certain absolute value, may have a negligible impact onthe data point values of the corresponding reconstructed data element.The basis function sets from ensemble of dictionaries learned bytraining visual information comprising data elements of visualinformation of a similar type and sparsity as the data elements to betransformed are expected to generate coefficient sets with a significantnumber of coefficients values close to zero. The present disclosureutilizes the fact that visual information is characteristically onlylocally sparse.

The method comprises a sparsification step, wherein coefficient valuesclose to zero are set to zero. The threshold below which the absolutevalue of coefficient values are truncated to zero may be based on apredetermined value determined during the creation of the ensemble ofdictionaries. The threshold below which the absolute value ofcoefficient values are truncated may be based on at least one normalizedabsolute value of the coefficient value's contribution to data points inthe data element. After the truncating step each coefficient set isexpected to contain a significant number of zero values, the coefficientset is said to be sparsified. The more coefficients that are sparsifiedto zero the smaller the size of the compressed coefficient set isexpected to be. The more coefficients that are sparsified to zero moreinformation of the visual information is lost and a largerreconstruction error is to be expected. Due to the inverse correlationbetween small size and loss of information the sparsification step maybe adjusted based on the type of visual information being sparsified.

The step of forming the representation of the visual information maycomprise compression of at least one coefficient set. The compression ofthe at least one coefficient set may comprise use of a compressionalgorithm.

The method may comprise a reconstruction step comprising selecting adata element and selecting at least one dimension for reconstruction ofdata points in said data element, and reconstructing at least one datapoint in the selected at least one dimension from the corresponding setof coefficients and dictionary of basis functions. Reconstruction of anindividual frame of a 2D video data set may require reconstruction of amultitude of data points from a multitude of data elements, however,reconstructing the complete data set may require a significantly largernumber of computations. The method may be arranged to transform a dataset comprising key frames and reconstruct a data set comprisingintermediate frames. Intermediate frames are frames between known keyframes, such as the extra frames generated if a 20 fps video of keyframes was transformed and reconstructed into a 60 fps video.

A reconstruction error of the method may be calculated by reconstructingat least part of a data element and comparing the reconstructed at leastpart of a data element with the corresponding original data element. Forcalculating reconstruction errors all data points of a data element maybe reconstructed for comparison.

The present disclosure further relates to a computer program forrepresenting a data set comprising n dimensional data elementsrepresenting visual information. The computer program comprises routinesfor performing the method according to the present disclosure. In oneexample, the computer program is an application on a sensor system.

The present disclosure further relates to a computer program product.The computer program product comprises a program code stored on areadable data storage medium for representing a data set comprising ndimensional data elements representing visual information. The datastorage medium may be non-volatile. The program code is configured toexecute the method according to the present disclosure. The computerprogram product may control a sensor system. Said computer programproduct may be an application for a sensor system.

The present disclosure further relates to a system for representing adata set comprising n dimensional data elements representing visualinformation, comprising a processor, a memory storage comprising acomputer program product, at least one visual information generatingdevice, wherein the processor is arranged to store data on the memorystorage, control the at least one visual information generating device,receive visual information from at least one visual informationgenerating device, run the computer program product arranged to executethe steps as discussed above, and provide the representation of thevisual information based on the sparsified coefficient sets. The systemmay be a personal computer. The system may comprise a sensor system. Thesystem may be a sensor system. The system may be a compressive sensorsystem, arranged to provide a significantly compressed representation ofthe captured visual information. The system may be a compressive lightfield camera. The processor may be arranged to control at least onevisual information generating device. At least one visual informationgenerating device may be a camera. At least one visual informationdevice may be a database comprising visual information. The system maycomprise hardware for wireless communication between the processor andat least one of the at least one visual information generating device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of an ensemble of dictionaries comprisingbasis functions.

FIG. 2 illustrates an example of a method for representing visualinformation.

FIG. 3 illustrates an example of a method for learning an ensemble ofdictionaries.

FIG. 4 illustrates an example of a system for representing visualinformation.

DETAILED DESCRIPTION

Throughout the figures, same reference numerals refer to same parts,concepts, and/or elements. Consequently, what will be said regarding areference numeral in one figure applies equally well to the samereference numeral in other figures unless not explicitly statedotherwise.

FIG. 1 illustrates an example of an ensemble 100 of dictionariescomprising basis functions 102.

The ensemble 100 comprises at least one dictionary 101. The ensemble 100may comprise a multitude of dictionaries. Each dictionary 101 comprisesat least one basis function 102. Each dictionary 101 may comprise amultitude of basis functions 102, forming a multi-dimensional dictionaryensemble 100.

The visual information comprises at least one data element 110, whichcomprises at least one data point 111. A data element 110 ofdimensionality n may be transformed by its assigned dictionary 101 ofbasis functions 102 to form a set of sparse coefficients 120representing the data element 110 in the transformation domain. Thesparse coefficients 120 may be saved to a memory storage, such as adatabase or a solid state drive. A set of sparse coefficients 120 andthe corresponding dictionary 101 may be used to form a reconstructeddata element 130. When reconstructing, all data points 131 of the dataelement 130 need not be reconstructed. At least one data point 131 ofthe data element 130 along at least one dimension may be selected forreconstruction. By comparing a data element 110 or at least one datapoint 111 with the corresponding reconstructed data element 130 or atleast one reconstructed data point 131 a reconstruction error may becalculated.

The ensemble 100 of dictionaries may be created by training theensembles of dictionaries on training visual information of a similartype as the visual information to be compressed, such as live action 2Dvideo or animated 2D video. The training visual information may compriseat least parts of the visual information to be compressed. The ensemble100 of dictionaries may be trained by dividing the training visualinformation into pre-clusters based on sparsity and learning apre-cluster ensemble of dictionaries for each pre-cluster. The obtainedpre-cluster ensemble of dictionaries is learned so that the pre-clusterensemble of dictionaries represents the pre-cluster of training visualinformation in the transformation domain with sparse sets ofcoefficients. The learned pre-cluster ensembles of dictionaries may becombined to form an ensemble of dictionaries 100.

FIG. 2 illustrates an example of a method 200 for representing visualinformation.

The method 200 comprises a step 210 of obtaining visual informationcomprising at least one data element 110. The visual information may bemulti-dimensional. The visual information may comprise computergenerated imagery. The visual information may be obtained from anon-volatile storage medium. The visual information may be obtainedcontinuously from a sensor system. The visual information may beobtained from at least one camera capturing a physical scene. The visualinformation may be data elements 110 and/or data points 111 based oninterpolation of sensor data. The visual information may be dataelements 110 and/or data points 111 based on interpolation of computergenerated imagery.

The method comprises a step 220 of obtaining an ensemble 100 ofdictionaries comprising at least one set of basis functions 102. Theensemble 100 of dictionaries comprises sets of basis functions 102arranged to transform data elements 110 of visual information to thetransformation domain. The sets of basis functions 102 may transform thevisual information into a transformation domain of equal or higherdimensionality. The dimensionality of the transformation domain, m, maybe equal to or larger than 2. A set of basis functions 102 and thecorresponding sets of coefficients 120 may independently represent thevisual information in its various dimensions in the transformationdomain. The step 220 of obtaining an ensemble 100 of dictionaries maycomprise creating and/or training and/or learning and/or modifying anensemble of dictionaries 100.

The method comprises a step 230 of assigning each data element 111 to adictionary 101. The step 230 of assigning each data element 111 to adictionary may assign data elements 111 to fewer dictionaries 101 thanthere are dictionaries 101 in the ensemble 100. The assignment of dataelements 111 to dictionaries 101 may be based on sparsity. Theassignment of data elements 111 to dictionaries 101 may comprise machinelearning.

The method comprises a step 240 of transforming the data elements 111 ofthe visual information into the transformation domain, wherein eachcoefficient set 120 and corresponding set of basis functions 102describes a data element 111 of the visual information. For an ensemble100 of dictionaries created based on training visual information of asimilar type and sparsity as the visual information to be transformed bythe basis function sets 102 are expected to generate coefficient sets120 with a significant number of coefficients 120 with values close tozero.

The method comprises a step 250 of sparsifying the coefficient sets 120.The sparsifying step 250 of the coefficient sets is an irreversible stepas information is permanently lost. The step 250 of sparsifying mayallow for an increased potential to compress the coefficient sets. Acorrelation is to be expected between the level of sparsification, thereconstruction error and the potential data compression ratio, however,a small reconstruction error and a large data compression ratio isdesired. The step 250 of sparsifying the coefficient sets 120 maycomprise setting the value of coefficients 120 close to zero to zero.The step 250 of sparsifying the coefficient sets 120 may comprisesetting the value of coefficients 120 with a low relative contributionto the data element of visual information to zero. The thresholds forsetting the value of coefficients 120 close to zero to zero may bedefined by the amount of tolerable error.

The method comprises a step 260 of forming the representation of thevisual information comprising a coefficient data set and thecorresponding dictionaries. The coefficient data set may comprise atleast one coefficient set. The step 260 of forming the representation ofthe visual information may comprise compression of at least onecoefficient set. The compression of the at least one coefficient set maycomprise a compression algorithm suitable for compressing sparse data.

FIG. 3 illustrates an example of a method 300 for learning an ensemble100 of dictionaries. The step 220 of obtaining an ensemble 100 ofdictionaries in the method 200 for representing visual information maycomprise the method 300 for learning an ensemble of dictionaries orsteps thereof.

The method 300 for learning an ensemble 100 of dictionaries comprises astep 310 of obtaining a training data set comprising training dataelements representing training visual information, a step 320 ofpre-clustering the training visual information into at least onepre-cluster, a step 330 of initiating a pre-cluster ensemble ofdictionaries for each pre-cluster, a step 340 of clustering the at leastone pre-cluster by assigning each training data element to a dictionaryin the corresponding pre-cluster ensemble, a step 350 of training the atleast one pre-cluster ensembles to minimize reconstruction error andmaximize sparsity of coefficients for the corresponding training dataelements, a step 360 of updating the clustering and iterating the step350 of training until at least one set of criteria is reached, and astep 370 of combining at least one of the at least one pre-clusterensemble to form an ensemble of dictionaries 100.

The method 300 for learning an ensemble 100 of dictionaries may be amachine learning assisted process. The step 310 of obtaining a trainingdata set may comprise a training data set of the same dimensionality andtype as the data elements 111 of visual information to be represented ina later stage by learned ensemble 100 of dictionaries. The step 320 ofpre-clustering the training visual information may be based on sparsity.The step 320 of pre-clustering the training visual information maycomprise machine learning. The step 330 of initiating a pre-clusterensemble of dictionaries for each pre-cluster may comprise initiatingpre-cluster ensembles based on the sparsity of the correspondingpre-cluster. The step 340 of clustering the at least one pre-cluster byassigning each training data element to a dictionary may be based onsparsity. The step 340 of clustering the at least one pre-cluster byassigning each training data element to a dictionary may comprisemachine learning. The step 350 of training the at least one pre-clusterensembles may be arranged to minimize the computational requirements forencoding and decoding. The step 360 of updating the clustering anditerating the step 350 of training may be arranged to minimize thenumber of dictionaries with training data elements assigned. The step370 of combining at least the at least one pre-cluster ensemble to forman ensemble of dictionaries 100 may comprise combining the at least onepre-cluster ensemble and at least one dictionary from at least one otherensemble 100 of dictionaries.

FIG. 4 illustrates an example of a system 400 for representing visualinformation. The system 400 for representing a data set comprising ndimensional data elements 110 representing visual information comprisinga processor 410, a memory storage 420 comprising a computer programproduct, at least one visual information generating device 430, whereinthe processor 410 is arranged to store data on the memory storage 420,receive visual information from at least one visual informationgenerating device 430, run the computer program product arranged toexecute the steps according to any of the claims 1 to 15, and providethe representation of the visual information based on the sparsifiedcoefficient sets 120. The system may be a personal computer. The systemmay comprise a sensor system. The system may be a sensor system. Theprocessor 410 may be arranged to control at least one visual informationgenerating device 430. At least one visual information generating device430 may be at least one camera. At least one visual information device430 may be a database comprising visual information. The system maycomprise hardware for wireless communication between the processor 410and the at least one visual information generating device 430.

The memory storage 420 is arranged to store a computer program productfor performing at least parts of the disclosed method described inrelation to FIG. 2 and FIG. 3. Said computer program product may atleast partly be run on said processor 410. Said computer program productmay comprise routines for controlling any of the at least one visualinformation generating device 430. Said computer program product maycomprise routines for wireless communication between the processor andthe at least one visual information generating device 430.

1-25. (canceled)
 26. A computer implemented method for representing adata set comprising at least one n dimensional data element (110)representing visual information, said method comprising the steps of:obtaining (210) said data set, obtaining (220) a dictionary ensemble(100) comprising a plurality of dictionaries (101) each comprising atleast one basis function (102), assigning (230) each at least one dataelement (110) to a dictionary (101), wherein a set of basis functions(102) represents an m dimensional transformation domain, transforming(240) the at least one data element (110) with the correspondingdictionary (101) of basis functions (102) to the transformation domainwherein each data element (110) is defined by an associated coefficientset (120), sparsifying (250) the coefficient sets (120), and forming(260) the representation of the visual information comprising acoefficient data set (120) and the corresponding dictionaries (101),wherein: the obtained dictionary ensemble (100) comprises an ensemble ofdictionaries formed from a combination of at least the at least onepre-cluster ensemble, each pre-cluster ensemble is obtained from atraining data set of training visual information comprising trainingdata elements, the training visual information is divided intopre-clusters, the pre-clusters are clustered by assigning each trainingdata element (110) to a dictionary of basis functions (102) in acorresponding pre-cluster ensemble of dictionaries for each pre-cluster,each pre-cluster ensemble is trained to minimize the reconstructionerror and maximize the sparseness of the training coefficient sets (120)of training data elements (110), and the clustering has been updated andthe training iterated until at least one criteria is fulfilled.
 27. Themethod according to according to claim 26, wherein the ensembleobtaining step (220) further comprises an ensemble learning step (300)comprising: obtaining (310) a training data set of training visualinformation comprising training data elements, dividing (320) thetraining visual information into pre-clusters, initializing (330) apre-cluster ensemble of dictionaries for each pre-cluster, clustering(340) the pre-clusters by assigning each training data elements to adictionary of basis functions in the corresponding pre-cluster ensemble,training (350) each pre-cluster ensemble to minimize the reconstructionerror and maximize the sparseness of the training coefficient sets oftraining data elements, updating (360) the clustering and iterating thetraining until at least one criteria is fulfilled, and combining (370)at least the at least one pre-cluster ensembles to form an ensemble ofdictionaries (100).
 28. The method according to claim 26, whereintransformation domain dimensionality, m, is at least 2 and wherein thesets of basis functions (102) independently represent each data element(110) in its various dimensions in the transformation domain.
 29. Themethod according to claim 26, wherein transformation domaindimensionality, m, is equal to or larger than the dimensionality of thevisual information, n, and wherein the sets of basis functions (102)independently represent each data element (110) in its variousdimensions in the transformation domain.
 30. The method according toclaim 26, further comprising a reconstruction step comprising: selectinga data element (130) and selecting at least one dimension forreconstruction of data points (131) in said data element (130), andreconstructing at least one data point (131) in the selected at leastone dimension from the corresponding set of coefficients (120) anddictionary (101) of basis functions.
 31. The method according to claim26, wherein the obtained data set comprises sensor data elements (110)and/or data points (111) based on interpolation of sensor data.
 32. Themethod according to claim 26, wherein the obtained data set comprisesdata elements (110) and/or data points (111) based on interpolation ofcomputer-generated imagery.
 33. The method according to claim 30,wherein the obtained data set comprises key frames and thereconstruction step is arranged to generate intermediate frames.
 34. Themethod according to claim 26, wherein the threshold for sparsifyingcoefficients is defined by the amount of tolerable error in the lossycompression.
 35. The method according to claim 26, wherein forming (260)the representation of the visual information comprises compressing atleast one sparsified coefficient set (120).
 36. A method according toclaim 27, wherein the pre-clustering is based on sparsity.
 37. A methodaccording to claim 27, wherein the at least one criteria is areconstruction error below a value and/or a sparsity value above acertain value.
 38. The method according to claim 27, wherein thelearning step (300) is based on machine learning.
 39. Acomputer-implemented method (300) for learning an ensemble (100) ofdictionaries, comprising: a step (310) of obtaining a training data setcomprising training data elements representing n dimensional trainingvisual information, a step (320) of dividing the training visualinformation into at least one pre-cluster, a step of creating apre-cluster ensemble for each pre-cluster of training visualinformation, and a step (370) of combining at least the at least onepre-cluster ensemble to form an ensemble (100) of dictionaries, whereinthe step of creating the at least one pre-cluster ensemble for eachpre-cluster comprises initiating (330) a pre-cluster ensemble ofdictionaries comprising basis functions for each pre-cluster, clustering(340) the at least one pre-cluster of training visual information byassigning each training data element to a dictionary of thecorresponding pre-cluster ensemble, training (350) each pre-clusterensemble to minimize the reconstruction error and maximize thesparseness of the training coefficient sets of training data elements,and updating (360) the clustering and iterating the training of thepre-cluster ensemble until at least one criteria is reached.
 40. Amethod according to claim39, wherein the dividing of the training visualinformation into at least one pre-cluster is based on sparsity.
 41. Amethod according to claim 39, wherein the at least one criteria is areconstruction error below a value and/or a sparsity value above acertain value.
 42. The method according to claim 39, wherein the step ofcreating the at least one pre-cluster ensemble is based on machinelearning.
 43. Use of a computer implemented method according to claim 26for compressed sensing, wherein the visual information is obtainedcontinuously from a sensor system.
 44. A computer program for performingthe method for representing a data set comprising n dimensional dataelements (110) representing visual information according to claim 26.45. A computer program product for performing the method forrepresenting a data set comprising n dimensional data elements (110)representing visual information according to claim
 26. 46. System (400)for representing a data set comprising n dimensional data elements (110)representing visual information, said system comprising: a processor(410), a memory storage (420), and at least one visual informationgenerating device (430), wherein the processor (410) is configured to:store data on the memory storage (420), receive visual information fromat least one visual information generating device (430), run a computerprogram arranged to execute the steps according to claim 26, and providethe representation of the visual information based on the sparsifiedcoefficient sets (120).
 47. The system according to claim 46, wherein atleast one visual information generating device (430) is a sensor systemcapturing a physical scene.
 48. The system according to claim 46,wherein at least one visual information generating device (430) is adatabase providing visual information.
 49. The system according to claim46, wherein at least one visual information generating device (430) is acomputer providing computer generated imagery.
 50. The system accordingto claim 46, wherein the processor (410) is arranged to control at leastone of the at least one visual information generating device (430).